The present research deals with the study of the symmetries of the design of interior spaces in fast food restaurants in terms of formality as it is an important element and plays a direct role in the spatial configuration, which is designed in both of its performance, aesthetic and expressive aspects. Since the choice of shapes is a complex subject that has many aspects imposed by functional and aesthetic correlations, the problem of the research is represented by the following question: (To what extent can the symmetries of the interior design be used in the spaces of fast food restaurants?)
The research acquires its importance by contributing to the addition of knowledge to researchers, scholars, companies and the specialized publ

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

 The aim of this paper, is to discuss several high performance training algorithms fall into two main categories. The first category uses heuristic techniques, which were developed from an analysis of the performance of the standard gradient descent algorithm. The second category of fast algorithms uses standard numerical optimization techniques such as: quasi-Newton . Other aim is to solve the drawbacks related with these training algorithms and propose an efficient training algorithm for FFNN

   In this research, an adaptive Canny algorithm using fast Otsu multithresholding method is presented, in which fast Otsu multithresholding method is used to calculate the optimum maximum and minimum hysteresis values and used as automatic thresholding for the fourth stage of the Canny algorithm.      The new adaptive Canny algorithm and the standard Canny algorithm (manual hysteresis value) was tested on standard image (Lena) and satellite image. The results approved the validity and accuracy of the new algorithm to find the images edges for personal and satellite images as pre-step for image segmentation.  
 

In many video and image processing applications, the frames are partitioned into blocks, which are extracted and processed sequentially. In this paper, we propose a fast algorithm for calculation of features of overlapping image blocks. We assume the features are projections of the block on separable 2D basis functions (usually orthogonal polynomials) where we benefit from the symmetry with respect to spatial variables. The main idea is based on a construction of auxiliary matrices that virtually extends the original image and makes it possible to avoid a time-consuming computation in loops. These matrices can be pre-calculated, stored and used repeatedly since they are independent of the image itself. We validated experimentally th